1,295 research outputs found
An Empirical Study of Link Quality Assessment in Wireless Sensor Networks applicable to Transmission Power Control Protocols
Transmission Power Control (TPC) protocols are poised for wide spread adoption in Wireless Sensor Networks (WSNs) to address energy constraints. Identifying the optimum transmission power is a significant challenge due to the complex and dynamic nature of the wireless transmission medium and this has resulted in several previous TPC protocols reporting poor reliability and energy efficiency in certain scenarios. In line with current studies, this study presents an empirical characterisation of the transmission medium in typical WSN environments. Through this, the sources of link quality degradation are identified and extensive empirical evidence of their effects are presented. The results highlight that low power wireless links are significantly affected by spatio-temporal factors with the severity of these factors being heavily dependent on environment
Controllable radio interference for experimental and testing purposes in wireless sensor networks
Abstract—We address the problem of generating customized, controlled interference for experimental and testing purposes in Wireless Sensor Networks. The known coexistence problems between electronic devices sharing the same ISM radio band drive the design of new solutions to minimize interference. The validation of these techniques and the assessment of protocols under external interference require the creation of reproducible and well-controlled interference patterns on real nodes, a nontrivial and time-consuming task. In this paper, we study methods to generate a precisely adjustable level of interference on a specific channel, with lowcost equipment and rapid calibration. We focus our work on the platforms carrying the CC2420 radio chip and we show that, by setting such transceiver in special mode, we can quickly and easily generate repeatable and precise patterns of interference. We show how this tool can be extremely useful for researchers to quickly investigate the behaviour of sensor network protocols and applications under different patterns of interference, and we further evaluate its performance
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
View additional affiliations
View references (107)
Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
A Unified Framework for SINR Analysis in Poisson Networks with Traffic Dynamics
We study the performance of wireless links for a class of Poisson networks,
in which packets arrive at the transmitters following Bernoulli processes. By
combining stochastic geometry with queueing theory, two fundamental measures
are analyzed, namely the transmission success probability and the meta
distribution of signal-to-interference-plus-noise ratio (SINR). Different from
the conventional approaches that assume independent active states across the
nodes and use homogeneous point processes to model the locations of
interferers, our analysis accounts for the interdependency amongst active
states of the transmitters in space and arrives at a non-homogeneous point
process for the modeling of interferers' positions, which leads to a more
accurate characterization of the SINR. The accuracy of the theoretical results
is verified by simulations, and the developed framework is then used to devise
design guidelines for the deployment strategies of wireless networks
Fine-grained Spatio-Temporal Distribution Prediction of Mobile Content Delivery in 5G Ultra-Dense Networks
The 5G networks have extensively promoted the growth of mobile users and
novel applications, and with the skyrocketing user requests for a large amount
of popular content, the consequent content delivery services (CDSs) have been
bringing a heavy load to mobile service providers. As a key mission in
intelligent networks management, understanding and predicting the distribution
of CDSs benefits many tasks of modern network services such as resource
provisioning and proactive content caching for content delivery networks.
However, the revolutions in novel ubiquitous network architectures led by
ultra-dense networks (UDNs) make the task extremely challenging. Specifically,
conventional methods face the challenges of insufficient spatio precision,
lacking generalizability, and complex multi-feature dependencies of user
requests, making their effectiveness unreliable in CDSs prediction under 5G
UDNs. In this paper, we propose to adopt a series of encoding and sampling
methods to model CDSs of known and unknown areas at a tailored fine-grained
level. Moreover, we design a spatio-temporal-social multi-feature extraction
framework for CDSs hotspots prediction, in which a novel edge-enhanced graph
convolution block is proposed to encode dynamic CDSs networks based on the
social relationships and the spatio features. Besides, we introduce the
Long-Short Term Memory (LSTM) to further capture the temporal dependency.
Extensive performance evaluations with real-world measurement data collected in
two mobile content applications demonstrate the effectiveness of our proposed
solution, which can improve the prediction area under the curve (AUC) by 40.5%
compared to the state-of-the-art proposals at a spatio granularity of 76m, with
up to 80% of the unknown areas
- …